Adversarial Machine Learning

This book provides a comprehensive introduction to building robust machine learning in adversarial environments, covering theory, tools, and practical techniques for investigating system security and performing robust data analysis. It discusses privacy-preserving mechanisms, near-optimal evasion of classifiers, and case studies on email spam and network security.

\n Format: Hardback
\n Length: 338 pages
\n Publication date: 21 February 2019
\n Publisher: Cambridge University Press
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This comprehensive introduction, penned by esteemed researchers, brings together all the theory and tools required for developing robust machine learning in adversarial environments. Uncover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, delve into the latest practical techniques for investigating system security and conducting robust data analysis, and gain insights into novel approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are explored in depth, with in-depth case studies on email spam and network security highlighting successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, as well as potential future directions, this groundbreaking work is a must-read for researchers, practitioners, and students in computer security and machine learning, as well as those interested in learning about the next phase of the cybersecurity arms race.

Introduction:
Machine learning has revolutionized the field of computer science, enabling systems to learn from data and make accurate predictions. However, the development of machine learning algorithms is not without its challenges, particularly in adversarial environments where an adversary may try to manipulate data or attack the system. In this comprehensive introduction, we will explore the theory and tools needed for building robust machine learning in adversarial environments.

Adversarial Examples:
Adversarial examples are examples of data that are designed to mislead a machine learning algorithm, causing it to make incorrect predictions. These examples can be created by an adversary with knowledge of the underlying data or by manipulating the data during training. Adversarial examples can be difficult to detect and can have serious consequences, such as financial losses, security breaches, or even harm to human life.

Defenses Against Adversarial Examples:
To defend against adversarial examples, researchers have developed a variety of techniques. These techniques include regularization, adversarial training, and transfer learning. Regularization is a technique that adds additional constraints to the machine learning model, making it more robust to noise and adversarial examples. Adversarial training involves training the machine learning model to defend against adversarial examples by generating adversarial examples and then training the model to recognize and classify them. Transfer learning is a technique that uses a pre-trained model to initialize a new model and then fine-tune it to adapt to a specific task.

Investigating System Security:
In addition to defending against adversarial examples, it is important to investigate system security. This involves analyzing the system to identify vulnerabilities and weaknesses that an adversary may exploit. This can be done using a variety of techniques, such as vulnerability scanning, penetration testing, and security audits.

Robust Data Analysis:
Robust data analysis is also essential for building robust machine learning in adversarial environments. This involves analyzing data to identify patterns and trends that may be useful for making predictions. This can be done using a variety of techniques, such as statistical analysis, machine learning, and data visualization.

Countermeasures Against Cyber-Attacks:
Finally, it is important to develop countermeasures against cyber-attacks. This involves developing strategies and technologies to prevent and detect cyber-attacks, as well as responding to them when they occur. This can be done using a variety of techniques, such as firewalls, antivirus software, intrusion detection systems, and security awareness training.

Conclusion:
Building robust machine learning in adversarial environments is a complex task that requires a combination of theory and practice. By understanding the principles of adversarial examples, defenses against adversarial examples, investigating system security, conducting robust data analysis, and developing countermeasures against cyber-attacks, researchers and practitioners can develop more secure and reliable machine learning systems.

\n Weight: 800g\n
Dimension: 250 x 179 x 21 (mm)\n
ISBN-13: 9781107043466\n \n